The causes of truck driver intent‐to‐quit: a best‐fit regression model

Purpose – Driver turnover has plagued the truckload industry in the USA since deregulation in 1980. Turnover in truck load firms averages above 100 percent in good economic times and over 40 percent in bad ones, costing the industry billions of dollars. This research sought a best‐fit regression model to show how a firm might control its own turnover. Design/methodology/approach – The paper distributed a survey questionnaire to 800 truck drivers at a large US truckload motor carrier. The questionnaire included over 50 items with a seven‐point Likert‐type scale from strongly agree to strongly disagree. The paper analyzed 309 usable responses using regression analysis with scaled scores on aspects of a driver's job. The dependent variable was intent to quit (ITQ). Findings – Research on truck driver turnover has focused on driver attitudes as determinants of ITQ, looking at drivers' attitudes that might lead to higher ITQ and higher turnover. In this research, drivers' attitudes toward top management and dispatchers did not influence ITQ, a surprising result that raises questions and suggests a new direction for research. Research limitations/implications – This research was conducted in only one firm. Its results may not generalize to all trucking firms, especially not to smaller firms. The method used, however, does generalize. Other firms may use this same approach to identify the causes of turnover in their organizations. Practical implications – This research demonstrated a method for researching the causes of driver turnover that are practical and accessible to firms of all sizes. Social implications – This research was conducted in only one firm. Its results may not generalize to all trucking firms, especially not to smaller firms. The method used, however, does generalize. Other firms may use this same approach to identify the causes of turnover in their organizations. Originality/value – The results of this research suggest that the relationship between drivers and the firm have changed, probably because of the extensive use of new technology. The methods used here will help large trucking firms allocate resources for driver management and retention. They may also help smaller firms understand the implications of investment in sophisticated technology.